The SFN with any temporal loss was trained in two steps. First, it was trained as an image stylization model as described in [1]}. We used the Adam [2]} optimizer with a learning rate of \(10^{-3}\)  and batch size 4 and optimized the model for 20k iterations. Second, the SFN was finetuned on the DAVIS training set with a certain temporal loss. The weights of the content loss and the style loss were kept the same as in the first step to ensure that the stylization did not change. The finetuning process required two epochs (3456 iterations) using the Adam optimizer with a learning rate of \(10^{-4}\)  and batch size 2 (a pair of successive frames). We observed that two epochs of training were sufficient to stabilize the videos. The first step took approximately 6 hours. The training time for the second step depended on the temporal loss but was always approximately 20 minutes. Without the temporal loss, we only trained the SFN in the first step.
